Previous work [1]}, [2]}, [3]}, [4]}, [5]}, [6]} addressed this question by designing automatic learning-based methods for bug localization and repair. However, to obtain the necessary amount of data needed for training, they often employed code mutants instead of real bug fixes. Mutants are generated by automatically injecting small code changes into existing code. As this process is automated, mutants can be easily obtained at large masses which is necessary for training effective learning-based models. However, a mutant may not represent a real bug which could ultimately bottleneck the performance of learning based bug localization repair.
